Detecting defective peel-bearing potatoes in a random mixture of defective and acceptable peel-bearing potatoes

ABSTRACT

A system and method of operation performing automated optical inspection to remove peel-bearing defective potato pieces from a random mixture of peel-bearing defective and acceptable potato pieces use near infrared light as a source of illumination. The system implements a method of illuminating the mixture with near infrared light, detecting light reflected by the potato pieces under inspection, identifying defective potato piece surface regions based on the detected reflections, and removing the defective items from the mixture. The system and method the system implements permit the inspection of peel-bearing potato pieces for the presence of peel covered and exposed defects.

RELATED APPLICATION

This is a division of application Ser. No. 08/665,078, filed Jun. 14,1996, now U.S. Pat. No. 5,884,775.

TECHNICAL FIELD

The present invention pertains to automated optical inspection andsorting systems and methods and, in particular, to systems and methodsfor removing defective potato pieces from a random mixture of acceptableand defective peel-bearing potato pieces.

BACKGROUND OF THE INVENTION

Automated optical inspection and sorting systems have been used toinspect and sort various target specimens including fruits andvegetables, processed meat, baked goods, and other foodstuffs; to removedifferent types of recyclable material; and to sort foreign or defectiveitems from supplies of wood chips. These systems typically employ videosystems with charge-coupled device line scan cameras to acquire imagesof target specimens moved on a conveyor belt across an optical scanningarea. Illumination of the specimens is generally provided by eitherbroad-spectrum tubular fluorescent lamps or rare gas discharge lamps.Signal processing circuitry identifies variations in the shade of targetspecimen images and sorts target specimens accordingly.

Shipments of potato pieces, such as raw french fries, from producersoften include defective pieces that may contain potato rot, potato eyes,or potato dark green flesh. It is desirable to remove such contaminantsbefore shipping potato pieces to consumers or fast food outlets. Theremoval of defective pieces also helps to establish the actual quantityof acceptable pieces in a shipment.

Traditionally, consumers have preferred that potato pieces, such asfrench fries, be prepared from potatoes that had been peeled prior tobeing cut into pieces. Because of this preference, potato piece sortingsystems built in the past have generally been configured to rejectpotato pieces bearing potato peel.

More recently, however, potato piece foods that are still peel bearinghave surged in popularity. For example, peel-bearing french fries havebecome steadily more available and more accepted over the past severalyears. It is possible that consumers perceive these products to be morehealthful and “natural” than their naked brethren.

Unfortunately, existing potato piece sorting systems are not very usefulfor removing peel-bearing defective potato pieces from a random mixtureof defective and acceptable peel-bearing pieces because, as noted, suchsystems are configured to reject all peel-bearing pieces. Moreover, thisinadequacy is not overcome by means of simple recalibration becausecurrent systems use broad band visible light, which makes it difficultto distinguish an otherwise acceptable peel-bearing potato piece regionfrom a potato piece region that suffers from “potato eye,” is blightedby potato rot, or is dark green. Moreover, because potato peel issubstantially opaque to visible spectrum light, a covering of peelinhibits defect inspection of a portion of the potato flesh.

SUMMARY OF THE INVENTION

An object of the present invention is, therefore, to provide a systemand method of automated optical inspection and sorting that distinguishpeel-bearing defective potato pieces in a random mixture of defectiveand acceptable peel-bearing potato pieces.

Another object of the present invention is to provide such a system andmethod that can examine a peel-bearing potato piece for peel covereddefects.

According to the present invention, a system and method of automatedoptical inspection and sorting utilize differences in reflectivity ofnear infrared light to distinguish between defective pieces andacceptable pieces of peel-bearing potato. In a preferred embodiment, asource of near infrared light illuminates a random mixture ofpeel-bearing potato pieces, identifies the defective pieces, and usesthe identification to sort the defective pieces from the mixture.

Near infrared light has the advantage that it is reflected quite well byboth peeled and peel-bearing potato. Furthermore, it is not reflectedwell by “potato eyes,” potato rot, or potato dark green flesh.Therefore, it is generally fairly easy to distinguish good white potatoflesh from defective potato flesh by illuminating the potato pieces withnear infrared light. Moreover, potato peel is somewhat transparent tonear infrared light; therefore, where a defective region is hiddenbeneath the peel, the infrared system may nevertheless identify thedefective region.

Additional objects and advantages of the present invention will beapparent from the following detailed description of a preferredembodiment, which proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram showing a side elevation view of an illustrativeoptical inspection system operable in accordance with the presentinvention;

FIG. 2 is an isometric view of the illumination system shown in FIG. 1,with parts removed for clarity;

FIG. 3 is a graph showing the comparative relationships amongreflectivities of peeled acceptable potato white flesh; peel-bearingacceptable potato white flesh, and peeled defective potato flesh inrelation to the spectral energy distribution of an argon gas dischargelamp over the 400-1100 nm wavelength range of FIG. 3; and

FIG. 4 is an enlarged top view of the conveyor belt shown in FIG. 1.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

FIGS. 1 and 2 show an illustrative automated optical inspection system10 suitable for carrying out a method according to the invention.Inspection system 10 may be of the on-belt specimen inspection andsorting type described in U.S. Pat. Nos. 4,738,175 to Little et al. fora DEFECT DETECTION SYSTEM and 5,085,325 to Jones et al. for a COLORSORTING SYSTEM AND METHOD. Both of these patents are assigned to theassignee of this patent application. Although inspection system 10 isshown as an on-belt inspection system, the methods of the invention canalso be carried out with the use off-belt inspection systems such asthat described in U.S. Pat. No. 5,305,894 granted to McGarvey for aCENTER SHOT SORTING SYSTEM AND METHOD, which is assigned to the assigneeof this patent application.

Inspection system 10 employs an illumination system 12 and an endlessconveyor belt 14 having a width of about 1.2 meters (48 inches) to movepotato pieces 16 as quickly as 2.5-3.0 meters per second (500-600 feetper minute) in a direction 18 across an illumination area 20. It wouldbe typical for many products that most of the pieces would have at leastsome peel-bearing surface portions 108 (FIG. 4).

A high-resolution line scan video camera 22 having a one-dimensionalpixel array scans potato pieces 16 as they pass through a scanning area24 within illumination area 20. Camera 22 uses a lens having a focallength that images the width across belt 14 of image scanning area 24onto the full width of its pixel array. Camera 22 is compatible withother system parameters such as belt speed and illumination intensityand provides 2048 pixels per scan; it thus resolves a distance of about0.6 mm (about 0.023 inch) across belt 14 into one pixel. The cameracompletes a scan once each millisecond. Camera 22 may include more thanone unit; e.g., it may be two lower-resolution (1024 pixel) camerasmounted side by side. A camera that can be used for this purpose is aSRC Black and White Infrared 1024 Pixel Camera made by SRC Vision, Inc.,P.O. Box 1666, Medford, Oreg., 95401, which is the assignee of thispatent application.

Camera 22 samples the intensity of light reflected by potato pieces 16and assigns a brightness value for each pixel (“pixel value”). Theintensity of each pixel value is a function of the spectral powerdistribution of the source of illumination, the spectral response of thelocation from which the camera is receiving light energy at the time thepixel value is detected (“pixel location”), and the spectral response ofcamera 22. A sorting data processor 26 processes image data generated byvideo camera 22 and arranges these data in image frames containing auser selectable number of scan lines.

A central data processor unit 28 linked with sorting data processor 26,conveyor belt 14, and a rejector unit 30 synchronizes the timing of theposition of potato pieces 16 to the operation of rejector unit 30.Rejector unit 30 sorts and removes specimens 16 rejected by sorting dataprocessor 26. With respect to potato piece samples 16, data processor 26rejects those pieces that have an above threshold number of poorlyreflective pixels. Poor reflectivity indicates the presence of a potatodefect such as an “eye,” potato green flesh, or potato rot.

Illumination system 12 includes multiple, preferably two, light sourceassemblies 34 positioned to project near infrared light across the widthof belt 14 at scanning area 24 in illumination area 20. Each lightsource assembly 34 includes one of two nonfluorescing rare gas dischargelamps 36 for emitting respective high-intensity light rays 37 ofwavelengths that reflect off an inner light-reflecting surface 38 of ashroud-like reflector structure 40 and are directed toward illuminationarea 20. Light rays 37 have a spectral power distribution shown in FIG.3. Lamps 36 are cooled by forced air.

Each of lamps 36 contains a rare or noble gas or a mixture of raregases. Each rare gas and each mixture of rare gases emits selectwavelengths of high-intensity illumination when ionized at the breakdownvoltage. Lamps 36 emit light rays 37 with an intensity approximately twoto three or more times that of conventional fluorescent sources. Theintensity of the light rays reflected from potato pieces 16 depends uponthe distance between a respective one of lamps 36 and potato pieces 16.Both argon and xenon are preferred gasses for use in the presentinvention. Lamps 36 are filled to a pressure of approximately 665 Pa(Newtons per square meter) (approximately 5 Torr). A preferred distancebetween each of lamps 36, and between lamps 36 and potato pieces 16 is15.24 cm (6 inches).

Reflector structure 40, which fits within and is supported by an outercovering 42 of each light source assembly 34, includes a housing 44 anda preferably hemi-elliptical reflector 48 secured within housing 44.Each of lamps 36 may be held in place by, for example, a pair of tubesockets 50 that are supported by a light source support member 52connected to frame 54. The length 55 of each of lamps 36 is generally afunction of and typically greater than length 32 of scanning area 24.

Each of lamps 36 is positioned within rectangular frame 54 so that itlies in a direction generally perpendicular to conveyor belt traveldirection 18 to illuminate potato pieces 16 as they are scanned by videocamera 22. Light rays 37 propagate directly toward illumination area 20.Light rays 37 also propagate toward and reflect from light-reflectingsurface 38 of hemi-elliptical reflector 48 toward illumination area 20.Hemi-elliptical reflectors 48 have lengths 74 that are about equal tolength 32 of scanning area 24 and about equal to or shorter than length55 of lamps 36. Because reflectors 48 are of hemi-elliptical shape,reflectors 48 produce a line focus of light rays 37 that strikeillumination area 20 and scanning area 24 on conveyor belt 14.

Lamps 36 also typically have a smaller diameter than the diameters ofconventional broad-spectrum fluorescent tubes. When used withhemi-elliptical reflectors, smaller diameter lamps more closelyapproximate a line source of illumination than larger diameter lamps.Line sources are more efficient than diffuse sources of illumination.

Preferably, an optically transmissive protective covering 56 enclosesreflector structure 40 to protect potato pieces 16 from debris fallingfrom a broken lamp 36. Also, hemi-elliptical reflector 48 supports apreformed aluminum substrate that carries on its inner surface 38 alight-reflective coating such as, for example, the “BV2 coating” having89 to 93 percent reflectivity, which is produced by Optical CoatingLabs, Inc. of Santa Rosa, Calif.

Lamps 36 are described in great detail in U.S. Pat. No. 5,440,127 toSquyres for METHOD AND APPARATUS FOR ILLUMINATING TARGET SPECIMENS ININSPECTION SYSTEMS, which is assigned to the assignee of this patentapplication.

Each of lamps 36 plugs into tube socket 50 of lamp fixture 80, which isdesigned to support such a lamp 36 and to supply electrical current toit.

FIG. 3 graphically shows a set of reflectivity measurements ofacceptable exposed potato white flesh (curve 60), acceptablepeel-bearing potato white flesh (curve 62), dark potato rot (curve 68),potato eye (curve 70), and dark green potato (curve 72) over a range ofwavelengths of visible and infrared light. An argon lamp light energyspectrum (curve 64) is shown superimposed on curves 60, 62, 68, 70, and72. Visible light has wavelengths ranging from about 400 nm to about 710nm; whereas near infrared light has wavelengths ranging from about 750nm to about 1100 nm.

FIG. 3 shows that it is possible to distinguish acceptable exposed orpeel-bearing potato white flesh from defective exposed or peel-bearingpotato flesh by using the light from an argon lamp. In the spectralrange containing most of the argon lamp light energy, curves 60 and 62for acceptable exposed and peel-bearing potato white flesh are quiteclose to each other. In addition, curves 60 and 62 are well separatedfrom curves 68, 70 and 72 showing the reflectivities of potato fleshwith defects.

In addition, the proximity of curve 60 to curve 62 in the infraredregion is a manifestation of the increased transmissivity of potato peelin infrared light as opposed to visible light. This transmissivitypermits the detection of peel covered defects. Infrared light rays 74penetrate through the potato peel and into the potato flesh, where theyare reflected at varying depths. If good white potato flesh liesunderneath the potato peel, a relatively strong return is reflected backthrough the peel toward camera 22. A peel covered defect, however, willbe less reflective of light rays 74 and will appear as a dark spot tocamera 22, thereby permitting detection and removal.

FIGS. 1, 2, and 4 show a preferred embodiment of a potato piece sortingsystem. Potato pieces 16 are continuously introduced onto the surface ofconveyor belt 14, which is preferably white. Because they are quiteslippery, potato pieces 16 typically slide past one another onto belt 14and therefore do not rest on top of or cover a portion of one another.Potato pieces 16 have exposed portions 106 and peel-bearing portions108. One of potato pieces 16 bears a defect 110.

Camera 22 repeatedly scans transversely across the width of conveyorbelt 14 gathering a sequence of light intensity samples, also referredto as “pixels,” each one corresponding to a unique scan position acrossbelt 14. A multiplicity of pixel locations 111 (shown at a greatlyenlarged scale for ease of description) are divided into pixel sets 112,114, 116, and 118, each of which corresponds to a separate camera scanacross belt 14. Because belt 14 moves in direction 18 as camera 22 isscanning repeatedly, camera 22 views an incrementally changed portion ofbelt 14 with each new scan. Therefore, pixel set 118 is detected priorto pixel set 116, which is detected prior to pixel set 114, which isdetected prior to pixel set 112.

Pixel values corresponding to pixel locations 111 on belt 14 will behigher than pixel values corresponding to locations 111 on acceptablepotato flesh, which, in turn, will be higher than values correspondingto locations 111 on defective potato flesh 110. Therefore, onlydefective portion 110 of potato pieces 16 need be identified. A lightintensity threshold is set to distinguish the pixel values correspondingto the reflectivity of peel-bearing potato from the pixel valuescorresponding to the reflectivity of defective potato flesh. Inaddition, a number-of-pixels threshold is set whereby if consecutivepixel values from locations 111 in a single scan numbering in excess ofthis threshold are each below the light intensity threshold, a defectivearea will be recognized by data processor 26.

Each pixel value is first compared with the light intensity threshold.When a first pixel value from pixel set 112, for example, is below thelight intensity threshold, a count is begun of all subsequentconsecutive pixel values that are below the light intensity threshold.If this count exceeds the number-of-pixels threshold, a rejection isdeclared by sorting data processor 26 and central data processor 28commands rejector unit 30 to remove the piece 16 bearing the defect.

Sorting data processor 26 and central data processor 28 are typicallydevices that comprise a microprocessor such as an Intel 80386® andsupporting circuitry. These data processors are widely available. Onepopular and widely used variety of this sort of data processor is anAdvanced Technology Processor Model 3220 sold by TMT Corp. of Houston,Tex. Rejector unit 30 most typically is comprised of a row of closelyspaced air blowers or “puffers” placed transversely to the direction 18of potato piece 16 movement and displaced slightly in direction 18 fromthe end of belt 14. These puffers are controlled so that when adefective potato piece 16 is lofted from the end of belt 14, a puff ofair knocks it into a “defect bin.” This kind of rejector unit is shownin earlier referenced US. Pat. No. 5,305,894.

Alternatively, data processor 26 could compare pixel values fromneighboring locations 111 in consecutive sets and use a two-dimensionalcriteria for declaring a defective area. For example, if pixels fromneighboring locations in pixel set 118 and pixel set 116 failed thelight intensity threshold, the number of such pixel values from pixelset 118 and pixel set 116 could be computed and compared to aalternative number-of-pixels threshold to declare a defective area.

Although lamps 36 are preferred sources of illumination, other sourcesof illumination in the near infrared can also be effective. Gasdischarge lamps with other gas mixtures could be used. Instead of or inaddition to gas discharge lamps, the illumination could be provided byone or more lasers. Gas lasers produce high-intensity emission at about904 nm and can be tuned to produce emissions at other wavelengths in thenear infrared by varying the trapping levels with additions of suitablephosphors. Such lasers would be especially useful with a camera 22 thatused a silicon detector. Nd:YAG (neodymium:yttrium-aluminum-garnet)lasers produce high-intensity emission at about 1064 nm.

The spectral energy distribution of the detected illumination may bedifferent from that of lamps 36. A silicon detector, which is preferablyused in camera 22, has maximum response at about 750 nm andsubstantially reduced response at about 400 nm and about 1100 nm.

It will be obvious to those having skill in the art that many changesmay be made to the above-described details of the preferred embodimentof the invention without departing from the underlying principlesthereof. For example, illumination system 12 may also comprise multiplevideo cameras 22, a single light source 36 and hemi-elliptical reflector48, and light source or sources 36 at various distances and angles fromconveyor belt 14. The scope of the present invention should, therefore,be determined only by the following claims.

I claim:
 1. In an automated optical inspection process, a method ofdetecting in potatoes having at least partly peel-covered flesh surfacesa presence of flesh surface regions in defective condition eitherunhidden or hidden by peel material, the potatoes included in a randommixture of peel-bearing acceptable and peel-bearing defective potatoes,comprising: illuminating with near infrared light a mixture of potatoeshaving potato flesh surfaces that include flesh surface regions in soundcondition and flesh surface regions in defective condition; detectingreflections of near infrared light from the mixture; and performing ananalysis of the detected reflections of near infrared light from themixture including those reflections that penetrated the peel-coveredflesh surfaces to interpret relatively high intensity detectedreflections of near infrared light from the mixture to ignore peel as adefect and indicate a presence of flesh surface regions in soundcondition, thereby to enable a determination of a presence of fleshsurface regions in defective condition.
 2. The method of claim 1, inwhich the analysis of the detected reflections is performed by treatingpeel material to be optically transparent to near infrared light.
 3. Themethod of claim 1, further comprising removing from the mixture potatoesdetermined as having flesh surface regions in defective condition. 4.The method of claim 1, in which the detecting of reflections of nearinfrared light is performed by a device that detects visible and nearinfrared light.
 5. The method of claim 1, in which a conveyor belthaving a length transports the mixture of potatoes for opticalinspection and in which the detecting of reflections of near infraredlight is performed by a camera that repeatedly scans transversely to thelength of the conveyor belt to form a sequence of sets of pixel values,each set of pixel values corresponding to a separate camera scan.
 6. Themethod of claim 5, in which the performing of an analysis of thedetected reflections to enable a determination of flesh surface regionsin defective conditions includes examining the sets of pixel values. 7.The method of claim 6, in which the performing of an analysis of thedetected reflections to enable a determination of flesh surface regionsin defective condition includes examining each set of pixel valuesseparately.
 8. The method of claim 6, in which the performing of ananalysis of the detected reflections to enable a determination of fleshsurface regions in defective condition includes examining each set ofpixel values in conjunction with its neighboring sets of pixel values.9. In an automated optical inspection process, a method of detecting inpotatoes having at least partly peel-covered flesh surfaces a presenceof flesh surface regions in defective condition either unhidden orhidden by peel material, the potatoes included in a random mixture ofpeel-bearing acceptable and peel-bearing defective potatoes, comprising:illuminating the mixture with light comprising wavelengths greater than180 nm, the potato flesh surface regions in defective condition thatare, respectively, more reflective and less reflective of the light, andthe peel material being at least partly transparent to the light so thata mere presence of peel material is not indicative of a defect;detecting reflections of light from the mixture; and analyzing thedetected reflections of light including those that penetrated thepeel-covered flesh surface to distinguish acceptable exposed orpeel-covered flesh surface regions from defective exposed orpeel-covered flesh surface regions in defective condition to identifythe latter as those of peel-bearing defective potatoes in the mixture.10. The method of claim 9, further comprising removing from the mixturepotatoes identified as having flesh surface regions in defectivecondition.
 11. The method of claim 9, in which the analyzing of thedetected reflections includes identifying reflections that have lessthan a predetermined intensity.
 12. The method of claim 9, in which thenear infrared light is emitted by a laser, an argon lamp, a xenon lamp,or a broad band lamp.
 13. The method of claim 9, in which the detectingof reflections of near infrared light is performed by a device thatdetects visible and near infrared light.
 14. The method of claim 9, inwhich a conveyor belt having a length transports the mixture of potatoesfor optical inspection and in which the detecting of reflections of nearinfrared light is performed by a camera that repeatedly scanstransversely to the length of the conveyor belt to form a sequence ofsets of pixel values, each set of pixel values corresponding to aseparate camera scan.
 15. The method of claim 14, in which the analyzingthe detected reflections of near infrared light to identify fleshsurface regions in defective condition includes examining the sets ofpixel values.
 16. The method of claim 15, in which the analyzing thedetected reflections of near infrared light to identify flesh surfaceregions in defective condition includes examining each set of pixelvalues separately.
 17. The method of claim 14, in which the analyzingthe detected reflections of near infrared light to identify fleshsurface regions in defective condition includes examining each set ofpixel values in conjunction with its neighboring sets of pixel values.